Suppression of AQP7 is Crucial for Proliferation and Lipid Metabolism in ccRCC

Suppression of AQP7 is Crucial for Proliferation and Lipid Metabolism in ccRCC | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Suppression of AQP7 is Crucial for Proliferation and Lipid Metabolism in ccRCC Jun Zhao, Rui Wang, Jiacheng Jin, Yingwei Bi, Xue Chen, Jianbo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4058796/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: It is well-established that reprogramming of lipid metabolism is a feature of clear cell renal cell carcinoma (ccRCC), which also acts as a driving force in oncogenesis induced by hypoxia-inducible factors (HIFs) in ccRCC. Aquaporin 7 (AQP7), a channel facilitates glycerol to pass through the membrane and induces a decrease in triglycerides (TGs) and glycerol in adipocytes. However, whether AQP7 takes a part in the lipid metabolism and malignant behaviors of ccRCC is still unclear. Methods: The prognosis and diagnostic value of AQP7 in ccRCC were first evaluated by bioinformatics methods including TCGA database, Cox regression, etc. The expression of AQP7 was tested with tissue microarray and IHC. After AQP7 was stably upregulated by lentivirus transfection, cell viability, colony formation, and flow cytometry were performed. According to GSEA, Nile red staining was then used to detect lipid droplet accumulation, and relevant mechanisms and pathways were verified through Western blotting and qPCR. Results: AQP7 was suppressed in both TCGA and the tissue microarray cohort, and the prognosis was worse for patients with lower AQP7 levels, including OS, DSS and PFI. Multiple lipid metabolism pathways, especially the PPARα pathway, were activated in the cohort with high AQP7 expression based on gene set enrichment analysis (GSEA). Moreover, AQP7 overexpression in ccRCC inhibited the proliferation ability, reduced the TG and glycerol contents, and led to cell cycle arrest. As a crucial transcription factor relevant to lipid metabolism, the ability of PPARα to bind to PPRE and the expression levels of PPARα, were both upregulated by AQP7 overexpression, as was the expression of a series of genes targeted by PPARα. Furthermore, downregulating HIF-1β and HIF-2α could elevate the expression levels of AQP7 and PPARα in ccRCC. Conclusions: AQP7 is suppressed in ccRCC and AQP7 may be a promising prognostic marker for the disease. Suppression of AQP7 in ccRCC contributes to lipid metabolism and cell cycle acceleration. The HIF/AQP7/PPARα axis might be an avenue for ccRCC treatment targeting lipid metabolism. ccRCC AQP7 prognosis lipid metabolism PPARα. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background In the urinary system, renal cell carcinoma ranks third on the list of most common malignant tumors [1]. Each year, approximately 175000 people worldwide die from renal cell carcinoma [2]. Clear cell renal cell carcinoma (ccRCC), the most prevalent form, is featured by abundant lipids and glycogen in cytoplasm and poor prognosis [3]. Studies have shown that ccRCC undergoes lipid metabolism reprogramming [4, 5]. Constitutive activation of hypoxia-inducible factors (HIFs) has been thought to be the primary driving force of reprogramming of lipid metabolism in ccRCC [6]. HIFs can inhibit carnitine palmitoyltransferase 1A (CPT1A), an enzyme responsible for β-oxidation, which further reduces the carnitine shuttle and promotes the deposition of fatty acids (FAs) in lipid droplets [7]. Lipidomics have indicated that triglycerides (TGs) and cholesterol esters (CEs) are both more abundant in ccRCC than normal renal cells [8]. TGs not only provide sufficient FAs to meet the elevated demand of ccRCC by decomposing into glycerol and FAs but also promote homeostasis by “buffering” specific FAs [9]. Although these metabolic changes appear to exert a profound influence on ccRCC tumorigenesis, the underlying molecular mechanism remains to be elucidated. Aquaporin 7 (AQP7), a pore-forming transmembrane protein belonging to the aquaglyceroporin family, can facilitate the permeation of water molecules and glycerol through the cell membrane [10]. In adipose tissue, an AQP7 deficiency results in adipocyte hypertrophy caused by an increasing accumulation of glycerol and TGs [11-13]. In recent years, AQP7 has been found to be abnormally expressed in pancreatic ductal adenocarcinoma, ovarian tumors, etc. [14-16]. According to a recent study, downregulation of AQP7 can lessen the glycerol excretion of breast cancer cells, accelerate lipid metabolism, and alter malignant behaviors [17]. It is unclear, however, what role AQP7 plays in lipogenesis in ccRCC. Peroxisome proliferator-activated receptor alpha (PPARα) can regulate many genes associated with lipid metabolism [18]. Upon activation by fatty acids (FAs), PPARα can bind to the peroxisome proliferator response element (PPRE), affecting downstream transcription including CPT1A, and inhibiting NF-κB activity [19]. Recent bioinformatics analysis combined with clinical samples indicated that PPARα is lower-expressed in cancerous tissues compared to precancerous tissues and correlates negatively with overall survival (OS) [20]. However, the interaction between AQP7 and PPARα keeps ambiguous. Herein, the expression of AQP7 and the prognostic and diagnostic significance of AQP7 was investigated in ccRCC. Moreover, whether AQP7 is modulated by HIF in ccRCC and how AQP7 regulates lipid metabolism and proliferation of ccRCC through PPARα were determined, which provides theoretical guidance for AQP7 as an intervention for ccRCC patients. Methods Bioinformatic analysis From the Cancer Genome Atlas (TCGA) database, 530 samples were accessed. Four GEO datasets (GSE6344/GSE11151/GSE43903/GSE36895) were recruited in the research. Differential transcriptional expression levels of AQP7 were evaluated according to the data classification method. A survival analysis was conducted, with OS, DSS and PFI as end points. Univariate Cox regression model development was performed by analyzing AQP7 expression levels, tumor dimension, AJCC stage, race, sex, grade, laterality, neoadjuvant treatment and age to filter significant variables for the multivariable Cox regression model. The calibration curves and the area under the ROC curve (AUC) were used to assess the nomogram after the multivariate Cox regression model was built. GenePattern software was used to conduct gene set enrichment analysis (GSEA) [20]. Tissue microarray technology Tissue microarrays were sourced from Shanghai Outdo Biotech Company (Shanghai, China, HKid-CRCC060PG-01). Cancer tissues and corresponding normal tissues recruited in the tissue microarrays were obtained from ccRCC patients undergoing surgery. For staining, DAB enzyme (Abcam, ab64238) was applied to stain protein, and the slides were counterstained with hematoxylin (Abcam, ab143166) for nuclei. Finally, two pathology experts read each tissue and rated the staining intensity and positive rate. The immunohistochemical (IHC) score was obtained as follows: [IHC score = staining intensity x positive rate], and the average of IHC scores from two pathology experts was taken as the final result. Cell culture The human ccRCC cell lines A498 and 786-O were obtained from the American Type Culture Collection (ATCC). A498 and 786-O cells were cultured in recommended medium with 10% fetal bovine serum and 1% penicillin/streptomycin. Human AQP7 cDNA was ectopically expressed by lentivirus transduction using pReceiver-Lv105 (Genecopia, USA). Cell transfection All shRNAs were purchased from GenePharma (Soochow, China). ccRCC cells were plated and cultured in a 6-well plate. The next morning, all transfections were performed using Lipofectamine 3000 (Life Technologies, USA). All functional assays were performed after cell infection. Cell viability assay Appropriate cells were seeded per well in 100 μl medium in a 96-well plate. At a specific time point, OD values were obtained in a microplate reader (Biotek, USA) after 10 μl Cell Counting Kit-8 (CCK-8) agent was added to the cells per well for 1.5 h. Colony formation assay Single-cell suspensions of appropriate ccRCC cells were seeded into a 60 mm plate. After colonies had grown sufficiently (10 days or so), crystal violet staining was applied to the cells. Finally, the result is quantified. Flow cytometry analysis The distribution of cell cycles was next measured. First, fixation with 75% ethanol was performed on appropriate cells at -20 °C. The next day, to stain the cells, propidium iodide staining buffer was used at a concentration of 50 μg/ml for half an hour. Finally, cells were kept in dark until being analyzed by a FACS system. PPARα transcription factor assay To measure the transcriptional activity of PPARα, a commercial ELISA kit (Abcam, ab133107) was applied. After nuclear extracts of ccRCC cells were collected, manufacturer's instructions were followed for the follow-up assay [21]. Determination of intracellular glycerol and triglyceride contents To detect the intracellular glycerol and triglyceride contents, a commercial kit (Applygen Technologies, China) was applied. After collecting the cells, manufacturer's instructions were followed for the follow-up assay. Nile red staining After 30 minutes of fixation with 4% paraformaldehyde, the cells in a 24-well plate were treated with 0.1% Triton X-100. After that, Hoechst 33342 (Beyotime, Beijing) and Nile red dye (1 g/ml, APExBIO, USA) were used to stain the nuclei and neutral lipids, respectively. Finally, photographs were taken. Western blotting The following antibodies were used: GAPDH (Proteintech), lamin B1 (Proteintech), AQP7 (Novus), PPARα (Abcam), HIF1B (Proteintech), HIF2A (Proteintech), CPT1A (Proteintech), cyclin D1 (Proteintech), CDK4 (Proteintech), IκBα (CST), p-IκBα (CST), IKKβ (CST), p-IKKα/β (CST), NF-κB (CST), p-NF-κB (CST). Briefly, after loading protein samples, wet transfer was conducted for appropriate time. The bands were then blocked with 3% nonfat milk at room temperature, following that, gentle incubation with diluted primary antibodies was conducted on a shaker overnight. Finally, after being immersed in HRP-linked secondary antibodies for 1 h, ECL mix was applied to expose the bands using Image Lab (Bio-Rad). Quantitative polymerase chain reaction (qPCR) By using Trizol, total RNA was extracted from the cells. After reverse transcription, the qPCRs were then performed in a TransStart Tip Green qPCR SuperMix system (TransGen Biotech, Beijing, China). The relative quantitative method was then adopted for analyzing, based on the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines [22]. Statistical analysis All laboratory data in the study came from at least three independent experiments and was analyzed by GraphPad Prism 9.0 software for statistical significance. Comparison between or among groups was performed using t-test or one-way ANOVA, respectively. Quantitative data are shown as the mean ± standard deviation (SD). Listed below are significant differences: *P < 0.05, **P < 0.01, ***P < 0.001. Results AQP7 is suppressed in ccRCC AQP7 mRNA was first detected from the TCGA database, as shown, AQP7 mRNA expression in ccRCC was found lower than noncancerous tissues (Fig. 1A). Further verification was performed in four additional GEO cohorts (GSE6344/GSE11151/GSE43903/GSE36895) (Fig. 1B). The relation between AQP7 and pathological characteristics was also explored. Interestingly, AQP7 mRNA expression in the ccB molecular subtype ccRCC, which has a worse clinical prognosis than the ccA molecular subtype, were lower than those in the ccA (Fig. 1C). Patients with lymphatic metastasis exhibited lower AQP7 mRNA expression levels than those without lymphatic metastasis (Fig. 1D), and with the increase in pathological grade, the AQP7 mRNA expression level decreased (Fig. 1E). The expression of AQP7 was further tested with tissue microarray and IHC, as compared to adjacent tissues, the staining intensity and IHC scores of AQP7 were clearly lower in ccRCC (Fig. 1F, G). Collectively, AQP7 was suppressed in ccRCC. Low AQP7 expression acts as a prognostic and diagnostic marker in ccRCC To further assess AQP7 expression distribution and survival status in ccRCC, according to median expression of AQP7 of 530 samples, two groups were formed. By plotting the survival curve and Kaplan‒Meier (KM) analysis method, a short OS (Fig. 2A), DSS (Fig. 2B), and PFI (Fig. 2C) was observed in patients expressing high levels of AQP7. To investigate whether AQP7 mRNA expression level is an independent prognostic variable in ccRCC patients, two univariate Cox regression models were developed, which revealed that AQP7 mRNA expression level acted as an independent prognostic variable for DSS (Table 1A) and PFI (Table 1B) in ccRCC. By using univariate Cox regression models, significant independent prognostic variables were screened. Next, two multivariate Cox regression models were constructed. In the multivariate Cox regression models, the AQP7 expression level remained significantly correlated with patients’ DSS (Table 1C) and PFI (Table 1D). These results suggested that AQP7 could be used for the prediction of DSS and PFI in ccRCC patients. Therefore, a ccRCC 3-year and 5-year DSS risk prediction nomogram was first built (Fig. 2D). For 3-year DSS (Fig. 2E) and 5-year DSS (Fig. 2F), a high degree of consistency was observed in the calibration curves. The ROC and AUC of the DSS nomogram demonstrated that the nomogram has a high discriminative ability for 3-year and 5-year DSS (Fig. 2G). A nomogram was also developed for predicting ccRCC 3-year and 5-year PFI according to the multivariate Cox regression model for PFI (Supplementary Fig. 1A). Similarly, calibration curves showed that the nomogram had high consistencies between the predicted and observed PFI probability for 3-year PFI (Supplementary Fig. 1B) and 5-year DSS (Supplementary Fig. 1C). The ROC and AUC of the PFI nomogram verified that the nomogram has a high discriminative ability for 3-year and 5-year PFIs (Supplementary Fig. 1D). Aiming to assess whether AQP7 expression can be used as a marker for ccRCC diagnosis, two ROC curves were generated based on AQP7 expression levels. The AUC of AQP7 expression was 0.87 in the TCGA cohort (Supplementary Fig. 1E); in a GEO cohort (GSE36895), it even reached 0.953, which is extremely high (Supplementary Fig. 1F). These results demonstrated that AQP7 expression has the ability to serve as a prognostic and diagnostic indicator for ccRCC. AQP7 overexpression inhibits ccRCC cell proliferation To identify the potential role that AQP7 may play in ccRCC, stable AQP7-overexpressing cell lines (786-O/A498-AQP7-OE#1, 786-O/A498-AQP7-OE#2) and empty vector cell lines (786-O/A498-AQP7-CTR) were successfully constructed. In Fig. 3A-B, overexpression efficiencies of AQP7 in 786-O cells were evaluated and verified by Western blotting and qPCR, respectively. A similar result was obtained in A498 cells, as shown in Fig. 3C-D. The CCK-8 assay in both cell lines indicated that when compared to the empty vector group, overexpression of AQP7 suppressed the proliferation of ccRCC cells (Fig. 3E-F). To further validate the findings, a colony formation assay was next conducted. As demonstrated in Fig. 3G, fewer colonies and a lower colony formation rate were observed after AQP7 was stably upregulated in ccRCC cells (Fig. 3G). Collectively, the results above suggest that AQP7 inhibited the proliferation of ccRCC cells. Overexpression of AQP7 promotes lipid metabolism through the PPARα signaling pathway in ccRCC cells With the assistance of GSEA, multiple lipid metabolism pathways were found activated in the high AQP7 expression groups, such as the adipocytokine signaling pathway, fatty acid metabolism, glycerophospholipid metabolism, and oxidative phosphorylation (Supplementary Fig. 2A-F). To figure out how AQP7 affects the neutral lipid content, Nile red staining was next performed. Intriguingly, the neutral lipid content was reduced in the AQP7 overexpression groups (Fig. 4A). To further explore the compositional changes in lipid metabolism, the effect of overexpression of AQP7 on the TG and glycerol content of ccRCC cells was detected, and it was found that AQP7 overexpression reduced both the TG and glycerol contents of ccRCC cells (Fig. 4B, C). These results above suggest that lipid accumulation was suppressed by overexpression of AQP7 in ccRCC cells. As GSEA suggested that the PPAR signaling pathway was activated in the cohort with high AQP7 expression (Supplementary Fig. 2F), AQP7 overexpression probably promoted lipid metabolism through the PPARα signaling pathway. The effect of AQP7 overexpression on PPARα protein and mRNA levels was first investigated, and it was detected that both PPARα mRNA and PPARα protein were significantly upregulated in AQP7-overexpressing 786-O cells (Fig. 4D-E). Analogous results in A498 cells are shown in Fig. 4F-G. Next, the ability of PPARα to bind to PPRE was tested. Using a commercial kit, it was found that the ability of PPARα to bind to PPRE was markedly strengthened by upregulating AQP7 in 786-O (Fig. 4I) and A498 (Fig. 4I) ccRCC cells. To examine whether AQP7 overexpression could affect the expression levels of PPARα target genes, 8 metabolism-related genes, which were demonstrated to be target genes of PPARα and in published research, were recruited for further study [23]. Using RT‒qPCR, it was found that all these genes, including CPT1A, PDK4, ACOX1, EHHADH, ACOT1, FABP1, HMGCS1, and ACAT1, were upregulated in AQP7-overexpressing 786-O and A498 cells (Supplementary Fig. 3A-B). Among the genes, the upregulation of CPT1A, the crucial enzyme of the carnitine shuttle for β-oxidation, was also verified at the protein level (Fig. 3C, D). Moreover, a positive correlation was found between AQP7 mRNA and all 8 PPARα target genes in the following gene correlation analysis in the TCGA clinical cohort, which also stabilized our results (Supplementary Fig. 3E). The results above indicated that AQP7 overexpression activates and upregulates PPARα as well as PPARα target genes. To further validate precisely whether AQP7 regulates the glycerol and TG content of ccRCC through PPARα, GW6471, a PPARα inhibitor [24], was administered to AQP7-overexpressing ccRCC cells. As expected, administration of GW6471 restored the reduction in TGs in AQP7-overexpressing 786-O and A498 cells. Following treatment with GW6471, no significant changes, however, were found in glycerol content in either AQP7-overexpressing ccRCC cell lines (Supplementary Fig. 3F-G). These results suggested that AQP7 could regulate the TG content of ccRCC cells through PPARα. In summary, AQP7 overexpression promoted lipid metabolism in ccRCC cells via the PPARα signaling pathway. AQP7 overexpression contributes to G1 phase cell cycle arrest The GSEA indicated that three cell cycle-related pathways, including the cancer pathway, P53 signaling pathway and cell cycle pathway, were activated in the low AQP7 expression groups besides lipid metabolism-related pathways (Supplementary Fig. 3G-I). Hence, AQP7 could probably affect the cell cycle in ccRCC. Flow cytometry analysis revealed that G1 phase was significantly arrested in AQP7-overexpressing 786-O cells (Fig. 5A-B). Moreover, Cyclin D1 and CDK4, the main cyclins involved in the regulation of G1 phase [25], were both obviously downregulated at the protein level in AQP7-overexpressing 786-O (Fig. 5C, D) and A498 cells (Supplementary Fig. 4A, B). These data implied that AQP7 overexpression arrested ccRCC cells in the G1 phase, thus affecting the proliferation of ccRCC cells. AQP7 overexpression inhibits the NF-κB signaling pathway Previous studies have reported that in addition to regulating lipid metabolism, PPARα induces the accumulation of IκBα to inhibit the NF-κB signaling, thus participating in anti-inflammatory action [26]. It was reasoned that AQP7 overexpression would likely suppress the NF-κB signaling, the western blotting assay was then performed. In support of the hypothesis, p-NF-κB were significantly reduced in ccRCC cells ectopically expressing AQP7 (Fig. 5E and 5F). The phosphorylation of NF-κB is mainly upregulated by IκBα, which is phosphorylated by the IKK complex [27]. Accordingly, the phosphorylation levels of IκBα, NF-κB and IKK complex were downregulated after AQP7 was upregulated in ccRCC cells (Fig. 5E and 5F). Furthermore, ccRCC cells transfected with AQP7 exhibited a distinct decreased level of NF-κB in nuclear lysates (Fig. 5G and 5H). These results suggest that AQP7 overexpression inhibited the NF-κB signaling pathway. The AQP7/PPARα axis is regulated by HIFs in ccRCC cells As the main driver event of ccRCC, sustained activation of HIFs is of paramount importance in ccRCC lipid metabolism and tumorigenesis. It was thus speculated that the AQP7/PPARα axis is regulated by HIFs. To test the hypothesis, HIF-2α, a HIF subunit, was first knocked down by transducing ccRCC cells with shRNA. AQP7 and PPARα were both detected and downregulated by qPCR and western blotting in HIF-2α knockdown 786-O (Fig. 6A-C) and A498 (Fig. 6D-F) cells. Likewise, ccRCC cells transduced with shRNA targeting HIF-1β, another subunit, showed a significant decrease for AQP7 mRNA and PPARα mRNA, as well as the protein levels (Fig. 6 G-L). The results above suggested that the AQP7/PPARα axis is inhibited by HIF-2α and HIF-1β in ccRCC cells. Discussion These data above indicate that AQP7 is suppressed in ccRCC and potentially serves as a prognostic and diagnostic marker for ccRCC. AQP7 overexpression epigenetically inhibits the proliferation of ccRCC. Upregulating AQP7 expression in ccRCC cells elevated PPARα expression levels and PPARα downstream lipid metabolism genes and thus decreased the TG and glycerin contents. In addition, AQP7 overexpression induced cell cycle arrest in ccRCC and suppressed the NF-κB signaling. Moreover, suppressing HIF-2α or HIF-1β expression upregulates AQP7 and PPARα expression levels. AQP7, a member of the AQP family, is suggested to be upregulated in hepatocellular carcinoma [16] and conversely downregulated in breast cancer [28]. Furthermore, another AQP family member, AQP9, has been demonstrated to be an oncogene in ccRCC [29]. Herein in ccRCC, AQP7 was expressed at significantly low levels, and the prognosis was worse for patients with lower AQP7 levels. Thus, AQP7 was combined with other variables to develop two nomograms, providing good predictability for survival in patients with ccRCC. The AUC and ROC methods further demonstrate that AQP7 expression is capable of identifying ccRCC tissue and normal renal tissue. All the above indicates that AQP7 might be a potential ccRCC diagnostic and prognostic biomarker. An important hallmark of ccRCC is altered lipid metabolism, which is known for a lipid storage feature [28]. Triglycerides (TGs) and cholesterol esters (CEs) are both verified to be more abundant in ccRCC by lipidomic [9]. AQP7, a glycerol transporter in adipocytes, facilitates glycerol efflux from adipocytes [30] and hence decreases the TG content of adipocytes. A recent study in breast cancer revealed that AQP7 disturbs the proliferation and lipid metabolism of metastatic cancer cells [17]. Herein, the GSEA showed that multiple lipid metabolism pathways, especially the PPARα pathway, were activated in the cohort with high AQP7 expression. AQP7 overexpression inhibits ccRCC proliferation and decreases glycerol and TG content in ccRCC. According to these results, suppressing AQP7 could take a meaningful part in maintaining continuous lipid accumulation and unlimited growth of ccRCC. In the future, artificially introducing AQP7 into ccRCC might give some help to ccRCC patients. PPARα, an important transcription factor, regulates a series of lipid metabolism processes[23]. A previous study revealed that PPARα was regulated by lncRNA-SLC16A1-AS1 and participated in promoting fatty acid β-oxidation [31]. Herein, the GSEA showed that multiple lipid metabolism pathways, especially the PPARα pathway, were activated in the cohort with high AQP7 expression. The results indicate that AQP7 overexpression elevates PPARα mRNA expression levels and CPT1A, the critical enzyme responsible for β-oxidation, which has been demonstrated to be directly regulated by PPARα [32]. In addition to CPT1A, 7 other representative PPARα targeting genes could still be upregulated by AQP7 overexpression. The correlation analysis further demonstrated that these 8 import PPARα targeting genes were relevant to AQP7. Moreover, the decrease in TG content induced by AQP7 overexpression could be restored by GW6471, a PPARα inhibitor. These results suggest that upregulating AQP7 promotes lipid metabolism by activating the PPARα pathway in ccRCC. Unscheduled cell cycle circulation induces infinite rapid duplication of ccRCC cells. Normal tissue cells generally maintain a high percentage of G0/G1 phase and a quiescent state. Conversely, most cancer cells re-enter mitogenic phases: G1, S, G2 and M. The results show that multiple cell cycle-related pathways were activated in the cohort with low AQP7 expression, and AQP7 overexpression contributed to G0/G1 phase cell cycle arrest. Consistently, ccRCC cells ectopically expressing AQP7 exhibit high levels of CDK4 and cyclin D1, both of which are key factors in promoting the G1/S cell cycle transition [33]. These results demonstrate that suppression of AQP7 is essential for inhibiting cell cycle arrest in ccRCC. A previous study indicated that PPARα-deficient mice showed accelerated cell regeneration and increased cyclin D1 protein levels [34], and the NF-κB signaling pathway induces and regulates cyclin D1 [35]. Another crucial biological function of PPARα is the suppression of inflammation [36], and inflammation has been identified as a pivotal oncogenesis event induced by sustaining HIF activation in ccRCC [37]. Moreover, PPARα contributes to the accumulation of IκBα in human cells by inhibiting the phosphorylation of IκBα [26]. It was thus hypothesized that the NF-κB signaling is suppressed by AQP7 overexpression. Supporting this hypothesis, the NF-κB protein levels in the nucleus are downregulated by AQP7 overexpression. The committed step of canonical NF-κB activation is phosphorylation-dependent activation of the IKKs (IκB kinases) complex [38]. Given that phosphorylated NF-κB p65 can enter the nucleus for transcriptional regulation [39], a significant reduction in phosphorylated IKK complex and NF-κB protein levels was observed in AQP7-overexpressing ccRCC cells. However, the level of IκBα phosphorylation was reduced, resulting in extensive accumulation of IκBα in ccRCC cells. According to these results, canonical NF-κB may be restrained by AQP7 overexpression through PPARα. Targeting AQP7 may be a promising approach to influencing a diversity of NF-κB-mediated biological processes, including inflammation and the cell cycle, in ccRCC. The continuous activation of HIF is a major driving force in lipid storage and inflammatory reactions in RCC cells [7, 40]. In cardiomyocytes, HIF accumulation can inhibit PPARα expression [41]. Herein, the results suggest that both AQP7 and PPARα could be upregulated by downregulation of HIF. Future studies will be needed to fully understand the mechanisms underlying this regulatory relationship. Comparisons with other studies and what does the current work add to the existing knowledge Targeted metabolism therapy has shown potential in tumor therapy. Herein, AQP7 has been involved in lipid metabolism and malignant behaviors of ccRCC. As a member of the aquaporin family, the cancer-suppressing effect of AQP7 in ccRCC was discovered for the first time except for transporting water. These provide some evidence for AQP7 acting as a prognostic monitoring indicator, these also provide some theoretical basis for the treatment of kidney cancer, especially interventions targeting lipid metabolism. Study strengths and limitations According to the current study, AQP7 might act as not only a prognostic marker but also a therapeutic target by combining bioinformatics analysis and basic experiments. AQP7, an aquaglyceroporin, was found to be responsible for malignant behaviors of ccRCC for the first time, in which some mechanisms regarding lipid metabolism regulation are involved. However, several limitations still exist. Due to the lack of sufficient references, the mechanism proposed here is not detailed and exhaustive enough, a richer and wider landscape about lipid composition also needs to be described in further study. Conclusion AQP7 is suppressed in ccRCC and the prognosis was worse for patients with lower AQP7 levels, including OS, DSS and PFI. Besides a prognostic marker, AQP7 might also be a therapeutic target. AQP7 overexpression inhibits the proliferation ability, promotes lipid metabolism, and results in cell cycle arrest in ccRCC. This research focused on the functions of AQP7, showing that AQP7 might be a biomarker to monitor the prognosis of ccRCC patients, also providing some evidence that the HIF/AQP7/PPARα axis might be an avenue for ccRCC treatment targeting lipid metabolism. Abbreviations ccRCC: clear cell renal cell carcinoma; HIFs: hypoxia-inducible factors; AQP7: aquaporin 7; TGs: triglycerides; OS: overall survival; DSS: disease-specific survival; PFI: progression-free interval; GSEA: gene set enrichment analysis; CPT1A: carnitine palmitoyltransferase 1A; FAs: fatty acids; CEs: cholesterol esters; PPARα: peroxisome proliferator-activated receptor alpha; PPRE: peroxisome proliferator response element; TCGA: The Cancer Genome Atlas; IHC: immunohistochemical; ATCC: American Type Culture Collection; CCK-8: Cell Counting Kit-8; KM: Kaplan‒Meier. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or during this study are included in this published article [and its supplementary files]. Competing interests The authors declare that they have no competing interests. Funding This work was funded by grants from the National Natural Science Foundation of China (No. 82173121). Authors’ contributions Jun Zhao and Rui Wang designed and performed the experiments, and wrote the manuscript; Jiacheng Jin, Yingwei Bi, and Xue Chen designed the figures; Jianbo Wang read and edited the manuscript. All authors approved the manuscript. Acknowledgments Not applicable. References Zhang Y, Chen M, Liu M, Xu Y, Wu G. Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma. Oxid Med Cell Longev. 2021;2021:6699808. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2018;68(6):394-424. Bokhari A, Tiscornia-Wasserman PG. Cytology diagnosis of metastatic clear cell renal cell carcinoma, synchronous to pancreas, and metachronous to thyroid and contralateral adrenal: Report of a case and literature review. Diagn Cytopathol. 2017;45(2):161-7. Lucarelli G, Loizzo D, Franzin R, Battaglia S, Ferro M, Cantiello F, et al. Metabolomic insights into pathophysiological mechanisms and biomarker discovery in clear cell renal cell carcinoma. Expert Rev Mol Diagn. 2019;19(5):397-407. Lucarelli G, Ferro M, Battaglia M. Multi-omics approach reveals the secrets of metabolism of clear cell-renal cell carcinoma. Transl Androl Urol. 2016;5(5):801-3. LaGory EL, Wu C, Taniguchi CM, Ding CC, Chi JT, von Eyben R, et al. Suppression of PGC-1alpha Is Critical for Reprogramming Oxidative Metabolism in Renal Cell Carcinoma. Cell Rep. 2015;12(1):116-27. Du W, Zhang L, Brett-Morris A, Aguila B, Kerner J, Hoppel CL, et al. HIF drives lipid deposition and cancer in ccRCC via repression of fatty acid metabolism. Nature communications. 2017;8(1):1769. Saito K, Arai E, Maekawa K, Ishikawa M, Fujimoto H, Taguchi R, et al. Lipidomic Signatures and Associated Transcriptomic Profiles of Clear Cell Renal Cell Carcinoma. Sci Rep. 2016;6:28932. Ackerman D, Tumanov S, Qiu B, Michalopoulou E, Spata M, Azzam A, et al. Triglycerides Promote Lipid Homeostasis during Hypoxic Stress by Balancing Fatty Acid Saturation. Cell Rep. 2018;24(10):2596-605 e5. Geng X, Yang B. Transport Characteristics of Aquaporins. Adv Exp Med Biol. 2017;969:51-62. Lebeck J. Metabolic impact of the glycerol channels AQP7 and AQP9 in adipose tissue and liver. J Mol Endocrinol. 2014;52(2):R165-78. Hara-Chikuma M, Sohara E, Rai T, Ikawa M, Okabe M, Sasaki S, et al. Progressive adipocyte hypertrophy in aquaporin-7-deficient mice: adipocyte glycerol permeability as a novel regulator of fat accumulation. J Biol Chem. 2005;280(16):15493-6. Hibuse T, Maeda N, Funahashi T, Yamamoto K, Nagasawa A, Mizunoya W, et al. Aquaporin 7 deficiency is associated with development of obesity through activation of adipose glycerol kinase. Proc Natl Acad Sci U S A. 2005;102(31):10993-8. Magouliotis DE, Tasiopoulou VS, Dimas K, Sakellaridis N, Svokos KA, Svokos AA, et al. Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma. Pancreatology. 2019;19(3):436-42. Yang JH, Yan CX, Chen XJ, Zhu YS. Expression of aquaglyceroporins in epithelial ovarian tumours and their clinical significance. J Int Med Res. 2011;39(3):702-11. Chen XF, Li CF, Lu L, Mei ZC. Expression and clinical significance of aquaglyceroporins in human hepatocellular carcinoma. Mol Med Rep. 2016;13(6):5283-9. Dai C, Charlestin V, Wang M, Walker ZT, Miranda-Vergara MC, Facchine BA, et al. Aquaporin-7 Regulates the Response to Cellular Stress in Breast Cancer. Cancer Res. 2020;80(19):4071-86. Varga T, Czimmerer Z, Nagy L. PPARs are a unique set of fatty acid regulated transcription factors controlling both lipid metabolism and inflammation. Biochim Biophys Acta. 2011;1812(8):1007-22. Dharancy S, Malapel M, Perlemuter G, Roskams T, Cheng Y, Dubuquoy L, et al. Impaired expression of the peroxisome proliferator-activated receptor alpha during hepatitis C virus infection. Gastroenterology. 2005;128(2):334-42. Luo Y, Chen L, Wang G, Qian G, Liu X, Xiao Y, et al. PPARalpha gene is a diagnostic and prognostic biomarker in clear cell renal cell carcinoma by integrated bioinformatics analysis. J Cancer. 2019;10(10):2319-31. Ghosh S, O'Connell JF, Carlson OD, Gonzalez-Mariscal I, Kim Y, Moaddel R, et al. Linoleic acid in diets of mice increases total endocannabinoid levels in bowel and liver: modification by dietary glucose. Obes Sci Pract. 2019;5(4):383-94. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611-22. Naiman S, Huynh FK, Gil R, Glick Y, Shahar Y, Touitou N, et al. SIRT6 Promotes Hepatic Beta-Oxidation via Activation of PPARalpha. Cell Rep. 2019;29(12):4127-43 e8. Xu HE, Stanley TB, Montana VG, Lambert MH, Shearer BG, Cobb JE, et al. Structural basis for antagonist-mediated recruitment of nuclear co-repressors by PPARalpha. Nature. 2002;415(6873):813-7. Massague J. G1 cell-cycle control and cancer. Nature. 2004;432(7015):298-306. Bougarne N, Weyers B, Desmet SJ, Deckers J, Ray DW, Staels B, et al. Molecular Actions of PPARalpha in Lipid Metabolism and Inflammation. Endocr Rev. 2018;39(5):760-802. Morais C, Gobe G, Johnson DW, Healy H. The emerging role of nuclear factor kappa B in renal cell carcinoma. The international journal of biochemistry & cell biology. 2011;43(11):1537-49. Weiss RH. Metabolomics and Metabolic Reprogramming in Kidney Cancer. Semin Nephrol. 2018;38(2):175-82. Xu WH, Shi SN, Xu Y, Wang J, Wang HK, Cao DL, et al. Prognostic implications of Aquaporin 9 expression in clear cell renal cell carcinoma. J Transl Med. 2019;17(1):363. Kishida K, Kuriyama H, Funahashi T, Shimomura I, Kihara S, Ouchi N, et al. Aquaporin adipose, a putative glycerol channel in adipocytes. J Biol Chem. 2000;275(27):20896-902. Logotheti S, Marquardt S, Gupta SK, Richter C, Edelhauser BAH, Engelmann D, et al. LncRNA-SLC16A1-AS1 induces metabolic reprogramming during Bladder Cancer progression as target and co-activator of E2F1. Theranostics. 2020;10(21):9620-43. Sugden MC, Bulmer K, Gibbons GF, Knight BL, Holness MJ. Peroxisome-proliferator-activated receptor-alpha (PPARalpha) deficiency leads to dysregulation of hepatic lipid and carbohydrate metabolism by fatty acids and insulin. Biochem J. 2002;364(Pt 2):361-8. Li Y, Xiao X, Chen H, Chen Z, Hu K, Yin D. Transcription factor NFYA promotes G1/S cell cycle transition and cell proliferation by transactivating cyclin D1 and CDK4 in clear cell renal cell carcinoma. Am J Cancer Res. 2020;10(8):2446-63. Xie G, Song Y, Li N, Zhang Z, Wang X, Liu Y, et al. Myeloid peroxisome proliferator-activated receptor alpha deficiency accelerates liver regeneration via IL-6/STAT3 pathway after 2/3 partial hepatectomy in mice. Hepatobiliary Surg Nutr. 2022;11(2):199-211. Singh A, Devkar R, Basu A. Myeloid Differentiation Primary Response 88-Cyclin D1 Signaling in Breast Cancer Cells Regulates Toll-Like Receptor 3-Mediated Cell Proliferation. Front Oncol. 2020;10:1780. Saibil SD, St Paul M, Laister RC, Garcia-Batres CR, Israni-Winger K, Elford AR, et al. Activation of Peroxisome Proliferator-Activated Receptors alpha and delta Synergizes with Inflammatory Signals to Enhance Adoptive Cell Therapy. Cancer Res. 2019;79(3):445-51. Hoefflin R, Harlander S, Schafer S, Metzger P, Kuo F, Schonenberger D, et al. HIF-1alpha and HIF-2alpha differently regulate tumour development and inflammation of clear cell renal cell carcinoma in mice. Nat Commun. 2020;11(1):4111. Israel A. The IKK complex, a central regulator of NF-kappaB activation. Cold Spring Harb Perspect Biol. 2010;2(3):a000158. DiDonato JA, Mercurio F, Karin M. NF-kappaB and the link between inflammation and cancer. Immunol Rev. 2012;246(1):379-400. Hsieh JJ, Le VH, Oyama T, Ricketts CJ, Ho TH, Cheng EH. Chromosome 3p Loss-Orchestrated VHL, HIF, and Epigenetic Deregulation in Clear Cell Renal Cell Carcinoma. J Clin Oncol. 2018:JCO2018792549. Krishnan J, Suter M, Windak R, Krebs T, Felley A, Montessuit C, et al. Activation of a HIF1alpha-PPARgamma axis underlies the integration of glycolytic and lipid anabolic pathways in pathologic cardiac hypertrophy. Cell Metab. 2009;9(6):512-24. Table Table 1 is available in the Supplementary Files section Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4058796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278552625,"identity":"0af7a51c-83b0-44f7-906f-8dc6cb830db4","order_by":0,"name":"Jun Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhao","suffix":""},{"id":278552626,"identity":"7e24dffc-d9e9-48db-a0ea-2bafce65905c","order_by":1,"name":"Rui Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wang","suffix":""},{"id":278552628,"identity":"e8d569d5-f8ec-450e-96d4-6a2bba77b0d3","order_by":2,"name":"Jiacheng Jin","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Jin","suffix":""},{"id":278552630,"identity":"4f2f8073-ae6e-4253-afe6-2bf55d905c35","order_by":3,"name":"Yingwei Bi","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingwei","middleName":"","lastName":"Bi","suffix":""},{"id":278552632,"identity":"c334c8fa-36bd-47e7-a399-31e771956906","order_by":4,"name":"Xue Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Chen","suffix":""},{"id":278552634,"identity":"f08f041f-d5f7-4ecc-8b3a-57341cae4ef0","order_by":5,"name":"Jianbo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYLACCQMGBgMG5gMSYN4B4rWwJZCgBQQMGHgMiNNicPzs4RcWBXZ55uw9H2/dbGOQ47uRwPi5AJ+WM3lpFhIGycWWPWc3W+e2MRhL3khglp6BR4vZgRwzAwkD5sQNN3K3SQO1ABkJbMw8+LScfwPSUg9UmfMMpKWesJYbOcYPJAwOg7SwgbQkGBDSYn/jjRkwkI8n7uw5Zmydc07CcOaZh83S+LRI9ucYf5b4U524nb354e2cMht5vuPJBz/j0wIEbNISCA6IydiAXwMDA/PHD4SUjIJRMApGwcgGALN8TVPZwYSiAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-09 17:45:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4058796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4058796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52624301,"identity":"142998da-dd59-47ab-89b8-2738419c370d","added_by":"auto","created_at":"2024-03-13 17:29:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1085634,"visible":true,"origin":"","legend":"\u003cp\u003eAQP7 is suppressed in ccRCC. (\u003cstrong\u003eA\u003c/strong\u003e) TCGA boxplots of AQP7 mRNA in cancerous and noncancerous tissues. (\u003cstrong\u003eB\u003c/strong\u003e) AQP7 mRNA in cancerous and noncancerous tissues from 4 GEO datasets. (\u003cstrong\u003eC\u003c/strong\u003e) AQP7 mRNA expression in different ccRCC subtypes of TCGA database. (\u003cstrong\u003eD\u003c/strong\u003e) Levels of AQP7 mRNA across different N stages in the TCGA database. (\u003cstrong\u003eE\u003c/strong\u003e) AQP7 mRNA levels in various grades using the Fuhrman grading system in the TCGA cohort. (\u003cstrong\u003eF\u003c/strong\u003e) Boxplots of IHC scores for AQP7 in cancerous tissues and paired noncancerous tissues. (\u003cstrong\u003eG\u003c/strong\u003e) Representative images of ccRCC tissues with Fuhrman grades of I-III by immunohistochemical staining for AQP7 compared with paired adjacent noncancerous tissues. Scale bar = 50 μm. *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/06cad02860589fd1d409a4df.png"},{"id":52623762,"identity":"1544fb94-7116-45ed-ad24-626b1d6d040f","added_by":"auto","created_at":"2024-03-13 17:21:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291382,"visible":true,"origin":"","legend":"\u003cp\u003eAQP7 is prognostic of survival in ccRCC. (\u003cstrong\u003eA-C\u003c/strong\u003e) Kaplan‒Meier (KM) curves of OS (A), DSS (B) and PFI (C) in two groups of patients. (\u003cstrong\u003eD\u003c/strong\u003e) A constructed nomogram for prediction of ccRCC 3-year and 5-year DSS after diagnosis. (\u003cstrong\u003eE \u003c/strong\u003eand\u003cstrong\u003e F\u003c/strong\u003e) Predicting DSS for 3-years (E) and 5-years (F) using the nomogram calibration curves. (\u003cstrong\u003eG\u003c/strong\u003e) ROC curves for the predictive nomogram to predict DSS probability within 3 years and 5 years.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/7b77b3b809c23e3d33ba02c6.png"},{"id":52623768,"identity":"e68a5c6e-0ed0-4839-a80b-a9dfff15a8d5","added_by":"auto","created_at":"2024-03-13 17:21:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":475002,"visible":true,"origin":"","legend":"\u003cp\u003eAQP7 overexpression inhibits ccRCC cell proliferation. (\u003cstrong\u003eA\u003c/strong\u003e) Determination of AQP7 expression levels in 786-O cells after being transfected with AQP7 lentivirus by western blotting (upper) and quantitative results of the protein levels (lower). “AQP7 OE” represents ccRCC transduced with AQP7 lentivirus. (\u003cstrong\u003eB\u003c/strong\u003e) In 786-O cells, determination of AQP7 mRNA levels after being transfected with AQP7 lentivirus by qPCR. (\u003cstrong\u003eC\u003c/strong\u003e) In A498 cells, determination of AQP7 protein levels after being transfected with AQP7 lentivirus (upper) and corresponding quantitative analysis (lower). (\u003cstrong\u003eD\u003c/strong\u003e) In A498 cells, determination of AQP7 mRNA levels after being transfected with AQP7 lentivirus by qPCR. (\u003cstrong\u003eE\u003c/strong\u003e and \u003cstrong\u003eF\u003c/strong\u003e) In 786-O (E) and A498 (F) cells, cell viability was measured after AQP7 was stably upregulated by CCK-8 assay. (\u003cstrong\u003eG\u003c/strong\u003e) In ccRCC cells, the colony formation ability after being transfected with AQP7 lentivirus was examined (left), and quantitative results are shown (right). *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/c32d33ce31f0dbcd37fe2c5f.png"},{"id":52623766,"identity":"aac23ccb-4366-4551-9c3e-548c6e05c1e3","added_by":"auto","created_at":"2024-03-13 17:21:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":656594,"visible":true,"origin":"","legend":"\u003cp\u003eAQP7 expedites lipid metabolism. (\u003cstrong\u003eA\u003c/strong\u003e) Determination of neutral lipid content in 786-O cells by Nile red array. Scale bar = 200 μm. “AQP7 OE” represents ccRCC transduced with AQP7 lentivirus. (\u003cstrong\u003eB \u003c/strong\u003eand\u003cstrong\u003e C\u003c/strong\u003e) Relative concentrations of TGs and glycerol were quantified in AQP7-overexpressing 786-O cells (B) and A498 cells (C). (\u003cstrong\u003eD\u003c/strong\u003e) Western blotting analysis of PPARα in the AQP7 overexpression groups and the vector group in 786-O cells (upper) and quantitative analysis of the proteins (lower). (\u003cstrong\u003eE\u003c/strong\u003e) In 786-O cells, the qPCR analysis of PPARα after AQP7 was upregulated. (\u003cstrong\u003eF\u003c/strong\u003e) Western blotting analysis of PPARα in AQP7 overexpression groups and the vector group in A498 cells (upper) and quantitative analysis of the proteins (lower). (\u003cstrong\u003eG\u003c/strong\u003e) qPCR analysis of PPARα after AQP7 was upregulated in A498 cells. (\u003cstrong\u003eH \u003c/strong\u003eand\u003cstrong\u003e I\u003c/strong\u003e) The ability of PPARα to bind to PPRE was detected by ELISA in AQP7-overexpressing 786-O (H) and AQP7-overexpressing A498 cells (I). *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/9b572bac0a701ffd858b13ed.png"},{"id":52623769,"identity":"323b2a0c-22a3-422a-a448-d59b11bd830d","added_by":"auto","created_at":"2024-03-13 17:21:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":353994,"visible":true,"origin":"","legend":"\u003cp\u003eAQP7 overexpression contributes to G1 phase cell cycle arrest and inhibits NF-κB signaling. (\u003cstrong\u003eA\u003c/strong\u003e) Cell cycle analyses of 786-O cells after transfection with AQP7 lentivirus. (\u003cstrong\u003eB\u003c/strong\u003e) Stack graph of the cell count of each cell cycle phase. (\u003cstrong\u003eC\u003c/strong\u003e) Cyclin D1 and CDK4 were detected in AQP7-overexpressing 786-O cells. (\u003cstrong\u003eD\u003c/strong\u003e) Quantitative results of cyclin D1 and CDK4 relative to Fig. 5C. (\u003cstrong\u003eE \u003c/strong\u003eand\u003cstrong\u003e F\u003c/strong\u003e) Western blotting analysis of NF-κB signaling pathway proteins, including IκBα, p-IκBα, IKKβ, p-IKKα/β, NF-κB, and p-NF-κB (left), and quantitative analysis of the proteins (right). (\u003cstrong\u003eG\u003c/strong\u003e and \u003cstrong\u003eH\u003c/strong\u003e) NF-κB in nucleus was measured by western blotting in AQP7-overexpressing ccRCC cells. *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/45a933d5343902c6ad2a4565.png"},{"id":52623770,"identity":"491bb19c-c9f9-44b5-9dda-1ff514b7a1e4","added_by":"auto","created_at":"2024-03-13 17:21:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":265866,"visible":true,"origin":"","legend":"\u003cp\u003eThe AQP7/PPARα axis was regulated by HIFs in ccRCC.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Western blotting analysis of HIF2A, AQP7 and PPARa in 786-O cells after transduction with shHIF2A. (\u003cstrong\u003eB\u003c/strong\u003e) Quantitative analysis of the proteins in Fig. 6A. (\u003cstrong\u003eC\u003c/strong\u003e) qPCR analysis of HIF2A, AQP7 and PPARa in 786-O cells after transduction with shHIF2a. (\u003cstrong\u003eD\u003c/strong\u003e) Western blotting analysis of HIF2A, AQP7 and PPARa in A498 cells after transduction with shHIF2a. (\u003cstrong\u003eE\u003c/strong\u003e) Quantitative analysis of the proteins in Fig. 6D. (\u003cstrong\u003eF\u003c/strong\u003e) qPCR analysis of HIF2a, AQP7 and PPARa in A498 cells after transduction with shHIF2a. (\u003cstrong\u003eG\u003c/strong\u003e) Western blotting analysis of HIF1B, AQP7 and PPARa in 786-O cells after transduction with shHIF1B. (\u003cstrong\u003eH\u003c/strong\u003e) Quantitative analysis of the proteins in Fig. 6G. (\u003cstrong\u003eI\u003c/strong\u003e) qPCR analysis of HIF1B, AQP7 and PPARa in 786-O cells after transduction with shHIF1B. (\u003cstrong\u003eJ\u003c/strong\u003e) Western blotting analysis of HIF1B, AQP7 and PPARa in A498 cells after transduction with shHIF1B. (\u003cstrong\u003eK\u003c/strong\u003e) Quantitative analysis of the proteins in Fig. 6J. (\u003cstrong\u003eL\u003c/strong\u003e) qPCR analysis of HIF1B, AQP7 and PPARa in A498 cells after transduction with shHIF1B. *, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/a7f7c447d5dd8a1132ac02aa.png"},{"id":52849126,"identity":"aaa9ca7d-0a37-470d-aa99-2b7d9ceb0667","added_by":"auto","created_at":"2024-03-17 21:07:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3631694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/3ba3e2b2-91ca-45ee-b6c5-2512492610e7.pdf"},{"id":52623765,"identity":"aa39df6b-50a2-49ae-a06a-2b77fb2fc821","added_by":"auto","created_at":"2024-03-13 17:21:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1415047,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.docx","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/0cf9172b93d9de1093d3d083.docx"},{"id":52623763,"identity":"de9ba27a-b6c8-44d7-bb9f-12fdffc8493b","added_by":"auto","created_at":"2024-03-13 17:21:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":164825,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-4058796/v1/ab2f31039e6c673b95729a7e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Suppression of AQP7 is Crucial for Proliferation and Lipid Metabolism in ccRCC","fulltext":[{"header":"Background","content":"\u003cp\u003eIn the urinary system, renal cell carcinoma ranks third on the list of most common malignant tumors [1]. Each year, approximately 175000 people worldwide die from renal cell carcinoma [2]. Clear cell renal cell carcinoma (ccRCC), the most prevalent form, is featured by abundant lipids and glycogen in cytoplasm and poor prognosis [3].\u003c/p\u003e\n\u003cp\u003eStudies have shown that ccRCC undergoes lipid metabolism reprogramming [4, 5]. Constitutive activation of hypoxia-inducible factors (HIFs) has been thought to be the primary driving force of reprogramming of lipid metabolism in ccRCC [6]. HIFs can inhibit carnitine palmitoyltransferase 1A (CPT1A), an enzyme responsible for β-oxidation, which further reduces the carnitine shuttle and promotes the deposition of fatty acids (FAs) in lipid droplets [7]. Lipidomics have indicated that triglycerides (TGs) and cholesterol esters (CEs) are both more abundant in ccRCC than normal renal cells [8]. TGs not only provide sufficient FAs to meet the elevated demand of ccRCC by decomposing into glycerol and FAs but also promote homeostasis by “buffering” specific FAs [9]. Although these metabolic changes appear to exert a profound influence on ccRCC tumorigenesis, the underlying molecular mechanism remains to be elucidated.\u003c/p\u003e\n\u003cp\u003eAquaporin 7 (AQP7), a pore-forming transmembrane protein belonging to the aquaglyceroporin family, can facilitate the permeation of water molecules and glycerol through the cell membrane [10]. In adipose tissue, an AQP7 deficiency results in adipocyte hypertrophy caused by an increasing accumulation of glycerol and TGs [11-13]. In recent years, AQP7 has been found to be abnormally expressed in pancreatic ductal adenocarcinoma, ovarian tumors, etc. [14-16]. According to a recent study, downregulation of AQP7 can lessen the glycerol excretion of breast cancer cells, accelerate lipid metabolism, and alter malignant behaviors [17]. It is unclear, however, what role AQP7 plays in lipogenesis in ccRCC.\u003c/p\u003e\n\u003cp\u003ePeroxisome proliferator-activated receptor alpha (PPARα) can regulate many genes associated with lipid metabolism [18]. Upon activation by fatty acids (FAs), PPARα can bind to the peroxisome proliferator response element (PPRE), affecting downstream transcription including CPT1A, and inhibiting NF-κB activity [19]. Recent bioinformatics analysis combined with clinical samples indicated that PPARα is lower-expressed in cancerous tissues compared to precancerous tissues and correlates negatively with overall survival (OS) [20]. However, the interaction between AQP7 and PPARα keeps ambiguous.\u003c/p\u003e\n\u003cp\u003eHerein, the expression of AQP7 and the prognostic and diagnostic significance of AQP7 was investigated in ccRCC. Moreover, whether AQP7 is modulated by HIF in ccRCC and how AQP7 regulates lipid metabolism and proliferation of ccRCC through PPARα were determined, which provides theoretical guidance for AQP7 as an intervention for ccRCC patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eBioinformatic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the Cancer Genome Atlas (TCGA) database, 530 samples were accessed. Four GEO datasets (GSE6344/GSE11151/GSE43903/GSE36895) were recruited in the research. Differential transcriptional expression levels of AQP7 were evaluated according to the data classification method. A survival analysis was conducted, with OS, DSS and PFI as end points. Univariate Cox regression model development was performed by analyzing AQP7 expression levels, tumor dimension, AJCC stage, race, sex, grade, laterality, neoadjuvant treatment and age to filter significant variables for the multivariable Cox regression model. The calibration curves and the area under the ROC curve (AUC) were used to assess the nomogram after the multivariate Cox regression model was built. GenePattern software was used to conduct gene set enrichment analysis (GSEA) [20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue microarray technology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue microarrays were sourced from Shanghai Outdo Biotech Company (Shanghai, China, HKid-CRCC060PG-01). Cancer tissues and corresponding normal tissues recruited in the tissue microarrays were obtained from ccRCC patients undergoing surgery. For staining, DAB enzyme (Abcam, ab64238) was applied to stain protein, and the slides were counterstained with hematoxylin (Abcam, ab143166) for nuclei. Finally, two pathology experts read each tissue and rated the staining intensity and positive rate. The immunohistochemical (IHC) score was obtained as follows: [IHC score = staining intensity x positive rate], and the average of IHC scores from two pathology experts was taken as the final result.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human ccRCC cell lines A498 and 786-O were obtained from the American Type Culture Collection (ATCC). A498 and 786-O cells were cultured in recommended medium with 10% fetal bovine serum and 1% penicillin/streptomycin. Human AQP7 cDNA was ectopically expressed by lentivirus transduction using pReceiver-Lv105 (Genecopia, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll shRNAs were purchased from GenePharma (Soochow, China). ccRCC cells were plated and cultured in a 6-well plate. The next morning, all transfections were performed using Lipofectamine 3000 (Life Technologies, USA). All functional assays were performed after cell infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell viability assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAppropriate cells were seeded per well in 100 μl medium in a 96-well plate. At a specific time point, OD values were obtained in a microplate reader (Biotek, USA) after 10 μl Cell Counting Kit-8 (CCK-8) agent was added to the cells per well for 1.5 h.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColony formation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell suspensions of appropriate ccRCC cells were seeded into a 60 mm plate. After colonies had grown sufficiently (10 days or so), crystal violet staining was applied to the cells. Finally, the result is quantified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of cell cycles was next measured. First, fixation with 75% ethanol was performed on appropriate cells at -20 °C. The next day, to stain the cells, propidium iodide staining buffer was used at a concentration of 50 μg/ml for half an hour. Finally, cells were kept in dark until being analyzed by a FACS system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPARα transcription factor assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo measure the transcriptional activity of PPARα, a commercial ELISA kit (Abcam, ab133107) was applied. After nuclear extracts of ccRCC cells were collected, manufacturer's instructions were followed for the follow-up assay [21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of intracellular glycerol and triglyceride contents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo detect the intracellular glycerol and triglyceride contents, a commercial kit (Applygen Technologies, China) was applied. After collecting the cells, manufacturer's instructions were followed for the follow-up assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNile red staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter 30 minutes of fixation with 4% paraformaldehyde, the cells in a 24-well plate were treated with 0.1% Triton X-100. After that, Hoechst 33342 (Beyotime, Beijing) and Nile red dye (1 g/ml, APExBIO, USA) were used to stain the nuclei and neutral lipids, respectively. Finally, photographs were taken.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following antibodies were used: GAPDH (Proteintech), lamin B1 (Proteintech), AQP7 (Novus), PPARα (Abcam), HIF1B (Proteintech), HIF2A (Proteintech), CPT1A (Proteintech), cyclin D1 (Proteintech), CDK4 (Proteintech), IκBα (CST), p-IκBα (CST), IKKβ (CST), p-IKKα/β (CST), NF-κB (CST), p-NF-κB (CST). Briefly, after loading protein samples, wet transfer was conducted for appropriate time. The bands were then blocked with 3% nonfat milk at room temperature, following that, gentle incubation with diluted primary antibodies was conducted on a shaker overnight. Finally, after being immersed in HRP-linked secondary antibodies for 1 h, ECL mix was applied to expose the bands using Image Lab (Bio-Rad).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative polymerase chain reaction (qPCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy using Trizol, total RNA was extracted from the cells. After reverse transcription, the qPCRs were then performed in a TransStart Tip Green qPCR SuperMix system (TransGen Biotech, Beijing, China). The relative quantitative method was then adopted for analyzing, based on the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll laboratory data in the study came from at least three independent experiments and was analyzed by GraphPad Prism 9.0 software for statistical significance. Comparison between or among groups was performed using t-test or one-way ANOVA, respectively. Quantitative data are shown as the mean ± standard deviation (SD). Listed below are significant differences: *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAQP7 is suppressed in ccRCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAQP7 mRNA was first detected from the TCGA database, as shown, AQP7 mRNA expression in ccRCC was found lower than noncancerous tissues (Fig. 1A). Further verification was performed in four additional GEO cohorts (GSE6344/GSE11151/GSE43903/GSE36895) (Fig. 1B). The relation between AQP7 and pathological characteristics was also explored. Interestingly, AQP7 mRNA expression in the ccB molecular subtype ccRCC, which has a worse clinical prognosis than the ccA molecular subtype, were lower than those in the ccA (Fig. 1C). Patients with lymphatic metastasis exhibited lower AQP7 mRNA expression levels than those without lymphatic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emetastasis (Fig. 1D), and with the increase in pathological grade, the AQP7 mRNA expression level decreased (Fig. 1E). The expression of AQP7 was further tested with tissue microarray and IHC, as compared to adjacent tissues, the staining intensity and IHC scores of AQP7 were clearly lower in ccRCC (Fig. 1F, G). Collectively, AQP7 was suppressed in ccRCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLow AQP7 expression acts as a prognostic and diagnostic marker in ccRCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further assess AQP7 expression distribution and survival status in ccRCC, according to median expression of AQP7 of 530 samples, two groups were formed. By plotting the survival curve and Kaplan‒Meier (KM) analysis method, a short OS (Fig. 2A), DSS (Fig. 2B), and PFI (Fig. 2C) was observed in patients expressing high levels of AQP7. To investigate whether AQP7 mRNA expression level is an independent prognostic variable in ccRCC patients, two univariate Cox regression models were developed, which revealed that AQP7 mRNA expression level\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eacted as an independent prognostic variable for DSS (Table 1A) and PFI (Table 1B) in ccRCC. By using univariate Cox regression models, significant independent prognostic variables were screened. Next, two multivariate Cox regression models were constructed.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn the multivariate Cox regression models, the AQP7 expression level remained significantly correlated with patients\u0026rsquo; DSS (Table 1C) and PFI (Table 1D). These results suggested that AQP7 could be used for the prediction of DSS and PFI in ccRCC patients. Therefore, a ccRCC 3-year and 5-year DSS risk prediction nomogram was first built (Fig. 2D). For 3-year DSS (Fig. 2E) and 5-year DSS (Fig. 2F), a high degree of consistency was observed in the calibration curves. The ROC and AUC of the DSS nomogram demonstrated that the nomogram has a high discriminative ability for 3-year and 5-year DSS (Fig. 2G). A nomogram was also developed for predicting ccRCC 3-year and 5-year PFI according to the multivariate Cox regression model for PFI (Supplementary Fig. 1A). Similarly, calibration curves showed that the nomogram had high consistencies between the predicted and observed PFI probability for 3-year PFI (Supplementary Fig. 1B) and 5-year DSS (Supplementary Fig. 1C). The ROC and AUC of the PFI nomogram verified that the nomogram has a high discriminative ability for 3-year and 5-year PFIs (Supplementary Fig. 1D).\u003c/p\u003e\n\u003cp\u003eAiming to assess whether AQP7 expression can be used as a marker for ccRCC diagnosis, two ROC curves were generated based on AQP7 expression levels. The AUC of AQP7 expression was 0.87 in the TCGA cohort (Supplementary Fig. 1E); in a GEO cohort (GSE36895), it even reached 0.953, which is extremely high (Supplementary Fig. 1F).\u003c/p\u003e\n\u003cp\u003eThese results demonstrated that AQP7 expression has the ability to serve as a prognostic and diagnostic indicator for ccRCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAQP7 overexpression inhibits ccRCC cell proliferation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the potential role that AQP7 may play in ccRCC, stable AQP7-overexpressing cell lines (786-O/A498-AQP7-OE#1, 786-O/A498-AQP7-OE#2) and empty vector cell lines (786-O/A498-AQP7-CTR) were successfully constructed. In Fig. 3A-B, overexpression efficiencies of AQP7 in 786-O cells were evaluated and verified by Western blotting and qPCR, respectively. A similar result was obtained in A498 cells, as shown in Fig. 3C-D. The CCK-8 assay in both cell lines indicated that when compared to the empty vector group, overexpression of AQP7 suppressed the proliferation of ccRCC cells (Fig. 3E-F). To further validate the findings, a colony formation assay was next conducted. As demonstrated in Fig. 3G, fewer colonies and a lower colony formation rate were observed after AQP7 was stably upregulated in ccRCC cells (Fig. 3G). Collectively, the results above suggest that AQP7 inhibited the proliferation of ccRCC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverexpression of AQP7 promotes lipid metabolism through the PPAR\u0026alpha; signaling pathway in ccRCC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the assistance of GSEA, multiple lipid metabolism pathways were found activated in the high AQP7 expression groups, such as the adipocytokine signaling pathway, fatty acid metabolism, glycerophospholipid metabolism, and oxidative phosphorylation (Supplementary Fig. 2A-F). To figure out how AQP7 affects the neutral lipid content, Nile red staining was next performed. Intriguingly, the neutral lipid content was reduced in the AQP7 overexpression groups (Fig. 4A). To further explore the compositional changes in lipid metabolism, the effect of overexpression of AQP7 on the TG and glycerol content of ccRCC cells was detected, and it was found that AQP7 overexpression reduced both the TG and glycerol contents of ccRCC cells (Fig. 4B, C). These results above suggest that lipid accumulation was suppressed by overexpression of AQP7 in ccRCC cells.\u003c/p\u003e\n\u003cp\u003eAs GSEA suggested that the PPAR signaling pathway was activated in the cohort with high AQP7 expression (Supplementary Fig. 2F), AQP7 overexpression probably promoted lipid metabolism through the PPAR\u0026alpha; signaling pathway. The effect of AQP7 overexpression on PPAR\u0026alpha; protein and mRNA levels was first investigated, and it was detected that both PPAR\u0026alpha; mRNA and PPAR\u0026alpha; protein were significantly upregulated in AQP7-overexpressing 786-O cells (Fig. 4D-E). Analogous results in A498 cells are shown in Fig. 4F-G. Next, the ability of PPAR\u0026alpha; to bind to PPRE was tested. Using a commercial kit, it was found that the ability of PPAR\u0026alpha; to bind to PPRE was markedly strengthened by upregulating AQP7 in 786-O (Fig. 4I) and A498 (Fig. 4I) ccRCC cells.\u003c/p\u003e\n\u003cp\u003eTo examine whether AQP7 overexpression could affect the expression levels of PPAR\u0026alpha; target genes, 8 metabolism-related genes, which were demonstrated to be target genes of PPAR\u0026alpha; and in published research, were recruited for further study [23]. Using RT‒qPCR, it was found that all these genes, including CPT1A, PDK4, ACOX1, EHHADH, ACOT1, FABP1, HMGCS1, and ACAT1, were upregulated in AQP7-overexpressing 786-O and A498 cells (Supplementary Fig. 3A-B). Among the genes, the upregulation of CPT1A, the crucial enzyme of the carnitine shuttle for \u0026beta;-oxidation, was also verified at the protein level (Fig. 3C, D). Moreover, a positive correlation was found between AQP7 mRNA and all 8 PPAR\u0026alpha; target genes in the following gene correlation analysis in the TCGA clinical cohort, which also stabilized our results (Supplementary Fig. 3E). The results above indicated that AQP7 overexpression activates and upregulates PPAR\u0026alpha; as well as PPAR\u0026alpha; target genes.\u003c/p\u003e\n\u003cp\u003eTo further validate precisely whether AQP7 regulates the glycerol and TG content of ccRCC through PPAR\u0026alpha;, GW6471, a PPAR\u0026alpha; inhibitor [24], was administered to AQP7-overexpressing ccRCC cells. As expected, administration of GW6471 restored the reduction in TGs in AQP7-overexpressing 786-O and A498 cells. Following treatment with GW6471, no significant changes, however, were found in glycerol content in either AQP7-overexpressing ccRCC cell lines (Supplementary Fig. 3F-G). These results suggested that AQP7 could regulate the TG content of ccRCC cells through PPAR\u0026alpha;.\u003c/p\u003e\n\u003cp\u003eIn summary, AQP7 overexpression promoted lipid metabolism in ccRCC cells via the PPAR\u0026alpha; signaling pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAQP7 overexpression contributes to G1 phase cell cycle arrest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GSEA indicated that three cell cycle-related pathways, including the cancer pathway, P53 signaling pathway and cell cycle pathway, were activated in the low AQP7 expression groups besides lipid metabolism-related pathways (Supplementary Fig. 3G-I). Hence, AQP7 could probably affect the cell cycle in ccRCC. Flow cytometry analysis revealed that G1 phase was significantly arrested in AQP7-overexpressing 786-O cells (Fig. 5A-B). Moreover, Cyclin D1 and CDK4, the main cyclins involved in the regulation of G1 phase [25], were both obviously downregulated at the protein level in AQP7-overexpressing 786-O (Fig. 5C, D) and A498 cells (Supplementary Fig. 4A, B). These data implied that AQP7 overexpression arrested ccRCC cells in the G1 phase, thus affecting the proliferation of ccRCC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAQP7 overexpression inhibits the NF-\u0026kappa;B signaling pathway\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies have reported that in addition to regulating lipid metabolism, PPAR\u0026alpha; induces the accumulation of I\u0026kappa;B\u0026alpha; to inhibit the NF-\u0026kappa;B signaling, thus participating in anti-inflammatory action [26]. It was reasoned that AQP7 overexpression would likely suppress the NF-\u0026kappa;B signaling, the western blotting assay was then performed. In support of the hypothesis, p-NF-\u0026kappa;B were significantly reduced in ccRCC cells ectopically expressing AQP7 (Fig. 5E and 5F). The phosphorylation of NF-\u0026kappa;B is mainly upregulated by I\u0026kappa;B\u0026alpha;, which is phosphorylated by the IKK complex [27]. Accordingly, the phosphorylation levels of I\u0026kappa;B\u0026alpha;, NF-\u0026kappa;B and IKK complex were downregulated after AQP7 was upregulated in ccRCC cells (Fig. 5E and 5F). Furthermore, ccRCC cells transfected with AQP7 exhibited a distinct decreased level of NF-\u0026kappa;B in nuclear lysates (Fig. 5G and 5H). These results suggest that AQP7 overexpression inhibited the NF-\u0026kappa;B signaling pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe AQP7/PPAR\u0026alpha; axis is regulated by HIFs in ccRCC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the main driver event of ccRCC, sustained activation of HIFs is of paramount importance in ccRCC lipid metabolism and tumorigenesis. It was thus speculated that the AQP7/PPAR\u0026alpha; axis is regulated by HIFs. To test the hypothesis, HIF-2\u0026alpha;, a HIF subunit, was first knocked down by transducing ccRCC cells with shRNA. AQP7 and PPAR\u0026alpha; were both detected and downregulated by qPCR and western blotting in HIF-2\u0026alpha; knockdown 786-O (Fig. 6A-C) and A498 (Fig. 6D-F) cells. Likewise, ccRCC cells transduced with shRNA targeting HIF-1\u0026beta;, another subunit, showed a significant decrease for AQP7 mRNA and PPAR\u0026alpha; mRNA, as well as the protein levels (Fig. 6 G-L). The results above suggested that the AQP7/PPAR\u0026alpha; axis is inhibited by HIF-2\u0026alpha; and HIF-1\u0026beta; in ccRCC cells.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThese data above indicate that AQP7 is suppressed in ccRCC and potentially serves as a prognostic and diagnostic marker for ccRCC. AQP7 overexpression epigenetically inhibits the proliferation of ccRCC. Upregulating AQP7 expression in ccRCC cells elevated PPAR\u0026alpha; expression levels and PPAR\u0026alpha; downstream lipid metabolism genes and thus decreased the TG and glycerin contents. In addition, AQP7 overexpression induced cell cycle arrest in ccRCC and suppressed the NF-\u0026kappa;B signaling. Moreover, suppressing HIF-2\u0026alpha; or HIF-1\u0026beta; expression upregulates AQP7 and PPAR\u0026alpha; expression levels.\u003c/p\u003e\n\u003cp\u003eAQP7, a member of the AQP family, is suggested to be upregulated in hepatocellular carcinoma [16] and conversely downregulated in breast cancer [28]. Furthermore, another AQP family member, AQP9, has been demonstrated to be an oncogene in ccRCC [29]. Herein in ccRCC, AQP7 was expressed at significantly low levels, and the prognosis was worse for patients with lower AQP7 levels. Thus, AQP7 was combined with other variables to develop two nomograms, providing good predictability for survival in patients with ccRCC. The AUC and ROC methods further demonstrate that AQP7 expression is capable of identifying ccRCC tissue and normal renal tissue. All the above indicates that AQP7 might be a potential ccRCC diagnostic and prognostic biomarker.\u003c/p\u003e\n\u003cp\u003eAn important hallmark of ccRCC is altered lipid metabolism, which is known for a lipid storage feature [28]. Triglycerides (TGs) and cholesterol esters (CEs) are both verified to be more abundant in ccRCC by lipidomic [9]. AQP7, a glycerol transporter in adipocytes, facilitates glycerol efflux from adipocytes [30] and hence decreases the TG content of adipocytes. A recent study in breast cancer revealed that AQP7 disturbs the proliferation and lipid metabolism of metastatic cancer cells [17]. Herein, the GSEA showed that multiple lipid metabolism pathways, especially the PPAR\u0026alpha; pathway, were activated in the cohort with high AQP7 expression. AQP7 overexpression inhibits ccRCC proliferation and decreases glycerol and TG content in ccRCC. According to these results, suppressing AQP7 could take a meaningful part in maintaining continuous lipid accumulation and unlimited growth of ccRCC. In the future, artificially introducing AQP7 into ccRCC might give some help to ccRCC patients.\u003c/p\u003e\n\u003cp\u003ePPAR\u0026alpha;, an important transcription factor, regulates a series of lipid metabolism processes[23]. A previous study revealed that PPAR\u0026alpha; was regulated by lncRNA-SLC16A1-AS1 and participated in promoting fatty acid \u0026beta;-oxidation [31]. Herein, the GSEA showed that multiple lipid metabolism pathways, especially the PPAR\u0026alpha; pathway, were activated in the cohort with high AQP7 expression. The results indicate that AQP7 overexpression elevates PPAR\u0026alpha; mRNA expression levels and CPT1A, the critical enzyme responsible for \u0026beta;-oxidation, which has been demonstrated to be directly regulated by PPAR\u0026alpha; [32]. In addition to CPT1A, 7 other representative PPAR\u0026alpha; targeting genes could still be upregulated by AQP7 overexpression. The correlation analysis further demonstrated that these 8 import PPAR\u0026alpha; targeting genes were relevant to AQP7. Moreover, the decrease in TG content induced by AQP7 overexpression could be restored by GW6471, a PPAR\u0026alpha; inhibitor. These results suggest that upregulating AQP7 promotes lipid metabolism by activating the PPAR\u0026alpha; pathway in ccRCC.\u003c/p\u003e\n\u003cp\u003eUnscheduled cell cycle circulation induces infinite rapid duplication of ccRCC cells. Normal tissue cells generally maintain a high percentage of G0/G1 phase and a quiescent state. Conversely, most cancer cells re-enter mitogenic phases: G1, S, G2 and M. The results show that multiple cell cycle-related pathways were activated in the cohort with low AQP7 expression, and AQP7 overexpression contributed to G0/G1 phase cell cycle arrest. Consistently, ccRCC cells ectopically expressing AQP7 exhibit high levels of CDK4 and cyclin D1, both of which are key factors in promoting the G1/S cell cycle transition [33]. These results demonstrate that suppression of AQP7 is essential for inhibiting cell cycle arrest in ccRCC. A previous study indicated that PPAR\u0026alpha;-deficient mice showed accelerated cell regeneration and increased cyclin D1 protein levels [34], and the NF-\u0026kappa;B signaling pathway induces and regulates cyclin D1 [35]. Another crucial biological function of PPAR\u0026alpha; is the suppression of inflammation [36], and inflammation has been identified as a pivotal oncogenesis event induced by sustaining HIF activation in ccRCC [37]. Moreover, PPAR\u0026alpha; contributes to the accumulation of I\u0026kappa;B\u0026alpha; in human cells by inhibiting the phosphorylation of I\u0026kappa;B\u0026alpha; [26]. It was thus hypothesized that the NF-\u0026kappa;B signaling is suppressed by AQP7 overexpression. Supporting this hypothesis, the NF-\u0026kappa;B protein levels in the nucleus are downregulated by AQP7 overexpression. The committed step of canonical NF-\u0026kappa;B activation is phosphorylation-dependent activation of the IKKs (I\u0026kappa;B kinases) complex [38]. Given that phosphorylated NF-\u0026kappa;B p65 can enter the nucleus for transcriptional regulation [39], a significant reduction in phosphorylated IKK complex and NF-\u0026kappa;B protein levels was observed in AQP7-overexpressing ccRCC cells. However, the level of I\u0026kappa;B\u0026alpha; phosphorylation was reduced, resulting in extensive accumulation of I\u0026kappa;B\u0026alpha; in ccRCC cells. According to these results, canonical NF-\u0026kappa;B may be restrained by AQP7 overexpression through PPAR\u0026alpha;. Targeting AQP7 may be a promising approach to influencing a diversity of NF-\u0026kappa;B-mediated biological processes, including inflammation and the cell cycle, in ccRCC.\u003c/p\u003e\n\u003cp\u003eThe continuous activation of HIF is a major driving force in lipid storage and inflammatory reactions in RCC cells [7, 40]. In cardiomyocytes, HIF accumulation can inhibit PPAR\u0026alpha; expression [41]. Herein, the results suggest that both AQP7 and PPAR\u0026alpha; could be upregulated by downregulation of HIF. Future studies will be needed to fully understand the mechanisms underlying this regulatory relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparisons with other studies and what does the current work add to the existing knowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTargeted metabolism therapy has shown potential in tumor therapy. Herein, AQP7 has been involved in lipid metabolism and malignant behaviors of ccRCC. As a member of the aquaporin family, the cancer-suppressing effect of AQP7 in ccRCC was discovered for the first time except for transporting water. These provide some evidence for AQP7 acting as a prognostic monitoring indicator, these also provide some theoretical basis for the treatment of kidney cancer, especially interventions targeting lipid metabolism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy strengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the current study, AQP7 might act as not only a prognostic marker but also a therapeutic target by combining bioinformatics analysis and basic experiments. AQP7, an aquaglyceroporin, was found to be responsible for malignant behaviors of ccRCC for the first time, in which some mechanisms regarding lipid metabolism regulation are involved. However, several limitations still exist. Due to the lack of sufficient references, the mechanism proposed here is not detailed and exhaustive enough, a richer and wider landscape about lipid composition also needs to be described in further study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAQP7 is suppressed in ccRCC and the prognosis was worse for patients with lower AQP7 levels, including OS, DSS and PFI. Besides a prognostic marker, AQP7 might also be a therapeutic target. AQP7 overexpression inhibits the proliferation ability, promotes lipid metabolism, and results in cell cycle arrest in ccRCC. This research focused on the functions of AQP7, showing that AQP7 might be a biomarker to monitor the prognosis of ccRCC patients, also providing some evidence that the HIF/AQP7/PPAR\u0026alpha; axis might be an avenue for ccRCC treatment targeting lipid metabolism.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eccRCC: clear cell renal cell carcinoma; HIFs: hypoxia-inducible factors; AQP7: aquaporin 7; TGs: triglycerides; OS: overall survival; DSS: disease-specific survival; PFI: progression-free interval; GSEA: gene set enrichment analysis; CPT1A: carnitine palmitoyltransferase 1A; FAs: fatty acids; CEs: cholesterol esters; PPAR\u0026alpha;: peroxisome proliferator-activated receptor alpha; PPRE: peroxisome proliferator response element; TCGA: The Cancer Genome Atlas; IHC: immunohistochemical; ATCC: American Type Culture Collection; CCK-8: Cell Counting Kit-8; KM: Kaplan‒Meier.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or during this study are included in this published article [and its supplementary files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by grants from\u0026nbsp;the National Natural Science Foundation of China (No. 82173121).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJun Zhao and Rui Wang designed and performed the experiments, and wrote the manuscript; Jiacheng Jin, Yingwei Bi, and\u0026nbsp;Xue\u0026nbsp;Chen\u0026nbsp;designed the figures; Jianbo Wang read and edited the manuscript. All authors approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang Y, Chen M, Liu M, Xu Y, Wu G. Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma. Oxid Med Cell Longev. 2021;2021:6699808.\u003c/li\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2018;68(6):394-424.\u003c/li\u003e\n\u003cli\u003eBokhari A, Tiscornia-Wasserman PG. Cytology diagnosis of metastatic clear cell renal cell carcinoma, synchronous to pancreas, and metachronous to thyroid and contralateral adrenal: Report of a case and literature review. Diagn Cytopathol. 2017;45(2):161-7.\u003c/li\u003e\n\u003cli\u003eLucarelli G, Loizzo D, Franzin R, Battaglia S, Ferro M, Cantiello F, et al. Metabolomic insights into pathophysiological mechanisms and biomarker discovery in clear cell renal cell carcinoma. Expert Rev Mol Diagn. 2019;19(5):397-407.\u003c/li\u003e\n\u003cli\u003eLucarelli G, Ferro M, Battaglia M. Multi-omics approach reveals the secrets of metabolism of clear cell-renal cell carcinoma. Transl Androl Urol. 2016;5(5):801-3.\u003c/li\u003e\n\u003cli\u003eLaGory EL, Wu C, Taniguchi CM, Ding CC, Chi JT, von Eyben R, et al. Suppression of PGC-1alpha Is Critical for Reprogramming Oxidative Metabolism in Renal Cell Carcinoma. Cell Rep. 2015;12(1):116-27.\u003c/li\u003e\n\u003cli\u003eDu W, Zhang L, Brett-Morris A, Aguila B, Kerner J, Hoppel CL, et al. HIF drives lipid deposition and cancer in ccRCC via repression of fatty acid metabolism. Nature communications. 2017;8(1):1769.\u003c/li\u003e\n\u003cli\u003eSaito K, Arai E, Maekawa K, Ishikawa M, Fujimoto H, Taguchi R, et al. Lipidomic Signatures and Associated Transcriptomic Profiles of Clear Cell Renal Cell Carcinoma. Sci Rep. 2016;6:28932.\u003c/li\u003e\n\u003cli\u003eAckerman D, Tumanov S, Qiu B, Michalopoulou E, Spata M, Azzam A, et al. Triglycerides Promote Lipid Homeostasis during Hypoxic Stress by Balancing Fatty Acid Saturation. Cell Rep. 2018;24(10):2596-605 e5.\u003c/li\u003e\n\u003cli\u003eGeng X, Yang B. Transport Characteristics of Aquaporins. Adv Exp Med Biol. 2017;969:51-62.\u003c/li\u003e\n\u003cli\u003eLebeck J. Metabolic impact of the glycerol channels AQP7 and AQP9 in adipose tissue and liver. J Mol Endocrinol. 2014;52(2):R165-78.\u003c/li\u003e\n\u003cli\u003eHara-Chikuma M, Sohara E, Rai T, Ikawa M, Okabe M, Sasaki S, et al. Progressive adipocyte hypertrophy in aquaporin-7-deficient mice: adipocyte glycerol permeability as a novel regulator of fat accumulation. J Biol Chem. 2005;280(16):15493-6.\u003c/li\u003e\n\u003cli\u003eHibuse T, Maeda N, Funahashi T, Yamamoto K, Nagasawa A, Mizunoya W, et al. Aquaporin 7 deficiency is associated with development of obesity through activation of adipose glycerol kinase. Proc Natl Acad Sci U S A. 2005;102(31):10993-8.\u003c/li\u003e\n\u003cli\u003eMagouliotis DE, Tasiopoulou VS, Dimas K, Sakellaridis N, Svokos KA, Svokos AA, et al. Transcriptomic analysis of the Aquaporin (AQP) gene family interactome identifies a molecular panel of four prognostic markers in patients with pancreatic ductal adenocarcinoma. Pancreatology. 2019;19(3):436-42.\u003c/li\u003e\n\u003cli\u003eYang JH, Yan CX, Chen XJ, Zhu YS. Expression of aquaglyceroporins in epithelial ovarian tumours and their clinical significance. J Int Med Res. 2011;39(3):702-11.\u003c/li\u003e\n\u003cli\u003eChen XF, Li CF, Lu L, Mei ZC. Expression and clinical significance of aquaglyceroporins in human hepatocellular carcinoma. Mol Med Rep. 2016;13(6):5283-9.\u003c/li\u003e\n\u003cli\u003eDai C, Charlestin V, Wang M, Walker ZT, Miranda-Vergara MC, Facchine BA, et al. Aquaporin-7 Regulates the Response to Cellular Stress in Breast Cancer. Cancer Res. 2020;80(19):4071-86.\u003c/li\u003e\n\u003cli\u003eVarga T, Czimmerer Z, Nagy L. PPARs are a unique set of fatty acid regulated transcription factors controlling both lipid metabolism and inflammation. Biochim Biophys Acta. 2011;1812(8):1007-22.\u003c/li\u003e\n\u003cli\u003eDharancy S, Malapel M, Perlemuter G, Roskams T, Cheng Y, Dubuquoy L, et al. Impaired expression of the peroxisome proliferator-activated receptor alpha during hepatitis C virus infection. Gastroenterology. 2005;128(2):334-42.\u003c/li\u003e\n\u003cli\u003eLuo Y, Chen L, Wang G, Qian G, Liu X, Xiao Y, et al. PPARalpha gene is a diagnostic and prognostic biomarker in clear cell renal cell carcinoma by integrated bioinformatics analysis. J Cancer. 2019;10(10):2319-31.\u003c/li\u003e\n\u003cli\u003eGhosh S, O\u0026apos;Connell JF, Carlson OD, Gonzalez-Mariscal I, Kim Y, Moaddel R, et al. Linoleic acid in diets of mice increases total endocannabinoid levels in bowel and liver: modification by dietary glucose. Obes Sci Pract. 2019;5(4):383-94.\u003c/li\u003e\n\u003cli\u003eBustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611-22.\u003c/li\u003e\n\u003cli\u003eNaiman S, Huynh FK, Gil R, Glick Y, Shahar Y, Touitou N, et al. SIRT6 Promotes Hepatic Beta-Oxidation via Activation of PPARalpha. Cell Rep. 2019;29(12):4127-43 e8.\u003c/li\u003e\n\u003cli\u003eXu HE, Stanley TB, Montana VG, Lambert MH, Shearer BG, Cobb JE, et al. Structural basis for antagonist-mediated recruitment of nuclear co-repressors by PPARalpha. Nature. 2002;415(6873):813-7.\u003c/li\u003e\n\u003cli\u003eMassague J. G1 cell-cycle control and cancer. Nature. 2004;432(7015):298-306.\u003c/li\u003e\n\u003cli\u003eBougarne N, Weyers B, Desmet SJ, Deckers J, Ray DW, Staels B, et al. Molecular Actions of PPARalpha in Lipid Metabolism and Inflammation. Endocr Rev. 2018;39(5):760-802.\u003c/li\u003e\n\u003cli\u003eMorais C, Gobe G, Johnson DW, Healy H. The emerging role of nuclear factor kappa B in renal cell carcinoma. The international journal of biochemistry \u0026amp; cell biology. 2011;43(11):1537-49.\u003c/li\u003e\n\u003cli\u003eWeiss RH. Metabolomics and Metabolic Reprogramming in Kidney Cancer. Semin Nephrol. 2018;38(2):175-82.\u003c/li\u003e\n\u003cli\u003eXu WH, Shi SN, Xu Y, Wang J, Wang HK, Cao DL, et al. Prognostic implications of Aquaporin 9 expression in clear cell renal cell carcinoma. J Transl Med. 2019;17(1):363.\u003c/li\u003e\n\u003cli\u003eKishida K, Kuriyama H, Funahashi T, Shimomura I, Kihara S, Ouchi N, et al. Aquaporin adipose, a putative glycerol channel in adipocytes. J Biol Chem. 2000;275(27):20896-902.\u003c/li\u003e\n\u003cli\u003eLogotheti S, Marquardt S, Gupta SK, Richter C, Edelhauser BAH, Engelmann D, et al. LncRNA-SLC16A1-AS1 induces metabolic reprogramming during Bladder Cancer progression as target and co-activator of E2F1. Theranostics. 2020;10(21):9620-43.\u003c/li\u003e\n\u003cli\u003eSugden MC, Bulmer K, Gibbons GF, Knight BL, Holness MJ. Peroxisome-proliferator-activated receptor-alpha (PPARalpha) deficiency leads to dysregulation of hepatic lipid and carbohydrate metabolism by fatty acids and insulin. Biochem J. 2002;364(Pt 2):361-8.\u003c/li\u003e\n\u003cli\u003eLi Y, Xiao X, Chen H, Chen Z, Hu K, Yin D. Transcription factor NFYA promotes G1/S cell cycle transition and cell proliferation by transactivating cyclin D1 and CDK4 in clear cell renal cell carcinoma. Am J Cancer Res. 2020;10(8):2446-63.\u003c/li\u003e\n\u003cli\u003eXie G, Song Y, Li N, Zhang Z, Wang X, Liu Y, et al. Myeloid peroxisome proliferator-activated receptor alpha deficiency accelerates liver regeneration via IL-6/STAT3 pathway after 2/3 partial hepatectomy in mice. Hepatobiliary Surg Nutr. 2022;11(2):199-211.\u003c/li\u003e\n\u003cli\u003eSingh A, Devkar R, Basu A. Myeloid Differentiation Primary Response 88-Cyclin D1 Signaling in Breast Cancer Cells Regulates Toll-Like Receptor 3-Mediated Cell Proliferation. Front Oncol. 2020;10:1780.\u003c/li\u003e\n\u003cli\u003eSaibil SD, St Paul M, Laister RC, Garcia-Batres CR, Israni-Winger K, Elford AR, et al. Activation of Peroxisome Proliferator-Activated Receptors alpha and delta Synergizes with Inflammatory Signals to Enhance Adoptive Cell Therapy. Cancer Res. 2019;79(3):445-51.\u003c/li\u003e\n\u003cli\u003eHoefflin R, Harlander S, Schafer S, Metzger P, Kuo F, Schonenberger D, et al. HIF-1alpha and HIF-2alpha differently regulate tumour development and inflammation of clear cell renal cell carcinoma in mice. Nat Commun. 2020;11(1):4111.\u003c/li\u003e\n\u003cli\u003eIsrael A. The IKK complex, a central regulator of NF-kappaB activation. Cold Spring Harb Perspect Biol. 2010;2(3):a000158.\u003c/li\u003e\n\u003cli\u003eDiDonato JA, Mercurio F, Karin M. NF-kappaB and the link between inflammation and cancer. Immunol Rev. 2012;246(1):379-400.\u003c/li\u003e\n\u003cli\u003eHsieh JJ, Le VH, Oyama T, Ricketts CJ, Ho TH, Cheng EH. Chromosome 3p Loss-Orchestrated VHL, HIF, and Epigenetic Deregulation in Clear Cell Renal Cell Carcinoma. J Clin Oncol. 2018:JCO2018792549.\u003c/li\u003e\n\u003cli\u003eKrishnan J, Suter M, Windak R, Krebs T, Felley A, Montessuit C, et al. Activation of a HIF1alpha-PPARgamma axis underlies the integration of glycolytic and lipid anabolic pathways in pathologic cardiac hypertrophy. Cell Metab. 2009;9(6):512-24.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ccRCC, AQP7, prognosis, lipid metabolism, PPARα.","lastPublishedDoi":"10.21203/rs.3.rs-4058796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4058796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e It is well-established that reprogramming of lipid metabolism is a feature of clear cell renal cell carcinoma (ccRCC), which also acts as a driving force in oncogenesis induced by hypoxia-inducible factors (HIFs) in ccRCC. Aquaporin 7 (AQP7), a channel facilitates glycerol to pass through the membrane and induces a decrease in triglycerides (TGs) and glycerol in adipocytes. However, whether AQP7 takes a part in the lipid metabolism and malignant behaviors of ccRCC is still unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe prognosis and diagnostic value of AQP7 in ccRCC were first evaluated by bioinformatics methods including TCGA database, Cox regression, etc. The expression of AQP7 was tested with tissue microarray and IHC. After AQP7 was stably upregulated by lentivirus transfection, cell viability, colony formation, and flow cytometry were performed. According to GSEA, Nile red staining was then used to detect lipid droplet accumulation, and relevant mechanisms and pathways were verified through Western blotting and qPCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e AQP7 was suppressed in both TCGA and the tissue microarray cohort, and the prognosis was worse for patients with lower AQP7 levels, including OS, DSS and PFI. Multiple lipid metabolism pathways, especially the PPARα pathway, were activated in the cohort with high AQP7 expression based on gene set enrichment analysis (GSEA). Moreover, AQP7 overexpression in ccRCC inhibited the proliferation ability, reduced the TG and glycerol contents, and led to cell cycle arrest. As a crucial transcription factor relevant to lipid metabolism, the ability of PPARα to bind to PPRE and the expression levels of PPARα, were both upregulated by AQP7 overexpression, as was the expression of a series of genes targeted by PPARα. Furthermore, downregulating HIF-1β and HIF-2α could elevate the expression levels of AQP7 and PPARα in ccRCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e AQP7 is suppressed in ccRCC and AQP7 may be a promising prognostic marker for the disease. Suppression of AQP7 in ccRCC contributes to lipid metabolism and cell cycle acceleration. The HIF/AQP7/PPARα axis might be an avenue for ccRCC treatment targeting lipid metabolism.\u003c/p\u003e","manuscriptTitle":"Suppression of AQP7 is Crucial for Proliferation and Lipid Metabolism in ccRCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 17:21:18","doi":"10.21203/rs.3.rs-4058796/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e14439d1-6394-4d69-b004-ac9473fb1ad8","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T01:57:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 17:21:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4058796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4058796","identity":"rs-4058796","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}